IMN Job Distribution Engine

Prototype Feedback Guide

Use this guide while trying the dry-run prototype the way you would use it in real work. The goal is to capture operational feedback that helps LightForge tune routing, review flow, and posting quality before IMN relies on it.

Dry run only No external postings Feedback-focused handoff

Purpose

Use your IMN judgment, not a software tester checklist.

Think like it is real

If this job went out tomorrow, would the title, boards, and content feel right?

Follow your instincts

If something feels off, confusing, too risky, or surprisingly good, that is useful feedback.

Look for fit

Does the workflow match how IMN reviews, routes, and approves jobs today?

You are not debugging the app

You are judging whether the prototype makes good operational decisions. Focus on real examples: a job that should route differently, a title that would not work, a screen that creates uncertainty, or a step that saves time.

Suggested walkthrough

Run one real-work loop, then look for edge cases.

1

Start with roles you know

Filter by division, board, specialty, and confidence. Search for jobs where you already have strong expectations.

2

Ask what you would do

Compare source data, generated title, target boards, issue chips, and preview content against your normal judgment.

3

Try the decision flow

Approve or reject a small sample as if it were a real review. Use Undo from Jobs if a decision was only for testing.

4

Notice patterns for the review

Keep track of repeated issues. One repeated routing mistake is more useful than ten isolated comments.

Primary workspace

Queue is where review decisions happen.

Use Queue to answer: should this job move forward, and are the system's assumptions correct?

Search Division Board Specialty Confidence

What to judge

Read the card like an operator, not a QA checklist.

Title

Is it board-ready, accurate, and free of internal shorthand?

Boards

Should this job go to these destinations, and only these destinations?

Issues

Do the flags point to real review concerns or false alarms?

Description

Does cleaned output preserve the important clinical and sales details?

Best feedback signal

"This should route to IAS and Gaswork because..." is more useful than "routing seems wrong."

Review actions

Use actions to test the workflow, then Undo test decisions.

Approve

Use when the job looks ready for its selected boards. In dry run, this records the decision only.

Reject to Exceptions

Use when a job needs correction before it should move forward.

Undo

Use Jobs search to find a reviewed job by ID, then restore it to Queue if the decision was just a test.

Do not bulk approve for feedback testing

Bulk actions are useful later. For this handoff, individual examples produce cleaner feedback and easier diagnosis.

Finding examples

Use filters to find patterns, then search to find specific jobs.

1

Start broad

Try division and board filters first: Medical, Dental, Clinical Magnet, IAS, DocCafe, Gaswork, JBoard.

2

Narrow by specialty

Use familiar categories like CRNA, Dentistry, Physical Therapist, or Nurse Practitioner.

3

Search exact examples

Use job ID, title terms, city, specialty, or board name when reporting a specific issue.

High-confidence currently shows zero jobs

Treat this as calibration feedback. If reviewers expect obvious jobs to be high-confidence, send those examples so the scoring model can be adjusted.

Audit views

Jobs and Activity answer "what happened?"

J

Jobs

Find reviewed jobs, confirm decision state, expand details, and Undo test reviews.

A

Activity

Review the event trail after approvals, rejects, retries, and other workflow actions.

E

Exceptions

Use this when a job is blocked or needs operator repair before moving forward.

Configuration review

Inspect Config for rule accuracy; edit only when asked.

Taxonomy

Are specialty mappings correct for each board's naming conventions?

Templates

Do title formats and footers match IMN's desired posting voice?

Compliance

Are do-not-publish and pay-transparency rules complete?

Feedback target

For config feedback, name the current rule, the desired rule, and one example job that proves why the change matters.

May 15, 2026 Zoom review

Bring your notes and talk through the prototype live.

What will happen

Each IMN reviewer will share their screen in the Zoom meeting and walk through what they noticed while testing.

Talk naturally: what felt good, what felt wrong, what was confusing, and what needs to change before the next pass.

1

Show the example

Pull up the job, screen, filter, or config area that made you react.

2

Say what you noticed

Call out what was good, bad, surprising, missing, or unclear.

3

Explain the business impact

Tell us whether it blocks real use, needs adjustment, or is a later idea.